SoroushVahidi/scidocs huggingface dataset:
The Cricket location-support system
(2000)

This paper presents the design, implementation, and evaluation of Cricket, a location-support system for in-building, mobile, location-dependent applications. It allows applications running on mobile and static nodes to learn their physical location by using listeners that hear and analyze information from beacons spread throughout the building. Cricket is the result of several design goals, including user privacy, decentralized administration, network heterogeneity, and low cost. Rather than explicitly tracking user location, Cricket helps devices learn where they are and lets them decide whom to advertise this information to; it does not rely on any centralized management or control and there is no explicit coordination between beacons; it provides information to devices regardless of their type of network connectivity; and each Cricket device is made from off-the-shelf components and costs less than U.S. $10. We describe the randomized algorithm used by beacons to transmit information, the use of concurrent radio and ultrasonic signals to infer distance, the listener inference algorithms to overcome multipath and interference, and practical beacon configuration and positioning techniques that improve accuracy. Our experience with Cricket shows that several location-dependent applications such as in-building active maps and device control can be developed with little effort or manual configuration.
Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study
(2017)

Influence maximization (IM) on social networks is one of the most active areas of research in computer science. While various IM techniques proposed over the last decade have definitely enriched the field, unfortunately, experimental reports on existing techniques fall short in validity and integrity since many comparisons are not based on a common platform or merely discussed in theory. In this paper, we perform an in-depth benchmarking study of IM techniques on social networks. Specifically, we design a benchmarking platform, which enables us to evaluate and compare the existing techniques systematically and thoroughly under identical experimental conditions. Our benchmarking results analyze and diagnose the inherent deficiencies of the existing approaches and surface the open challenges in IM even after a decade of research. More fundamentally, we unearth and debunk a series of myths and establish that there is no single state-of-the-art technique in IM. At best, a technique is the state of the art in only one aspect.
A 5.8-Gb/s Adaptive Integrating Duobinary DFE Receiver for Multi-Drop Memory Interface
(2017)

This paper describes a 5.8 Gb/s adaptive integrating duobinary decision-feedback equalizer (DFE) for use in next-generation multi-drop memory interface. The proposed receiver combines traditional interface techniques like the integrated signaling and the duobinary signaling, in which the duobinary signal is generated by current integration in the receiver. It can address issues such as input data dependence during integration, need for precursor equalization, high equalizer gain boosting, and sensitivity to high-frequency noise. The proposed receiver also alleviates DFE critical timing to provide gain in speed, and embed DFE taps in duobinary decoding to provide gain in power and area. The adaptation for adjusting the equalizer common-mode level, duobinary zero level, tap coefficient values, and timing recovery is incorporated. The proposed DFE receiver was fabricated in a 45 nm CMOS process, whose measurement results indicated that it worked at 5.8 Gb/s speed in a four-drop channel configuration with seven slave ICs, and the bathtub curve shows 36% open for  $10^{-10}$  bit error rate.
A Relatively Small Turing Machine Whose Behavior Is Independent of Set Theory
(2016)

Since the definition of the busy beaver function by Rado in 1962, an interesting open question has been the smallest value of n for which BB(n) is independent of Zermelo–Fraenkel set theory with the axiom of choice (ZFC). Is this n approximately 10, or closer to 1 000 000, or is it even larger? In this paper, we show that it is at most 7910 by presenting an explicit description of a 7910-state Turing machine Z with one tape and a two-symbol alphabet that cannot be proved to run forever in ZFC (even though it presumably does), assuming ZFC is consistent. The machine is based on work of Harvey Friedman on independent statements involving order-invariant graphs. In doing so, we give the first known upper bound on the highest provable busy beaver number in ZFC. To create Z, we develop and use a higher-level language, Laconic, which is much more convenient than direct state manipulation. We also use Laconic to design two Turing machines, G and R, that halt if and only if there are counterexamples to Goldbach’s conjecture and the Riemann hypothesis, respectively.
Graph analytics using vertica relational database
(2015)

Graph analytics is becoming increasingly popular, with a number of new applications and systems developed in the past few years. In this paper, we study Vertica relational database as a platform for graph analytics. We show that vertex-centric graph analysis can be translated to SQL queries, typically involving table scans and joins, and that modern column-oriented databases are very well suited to running such queries. Furthermore, we show how developers can trade memory footprint for significantly reduced I/O costs in Vertica. We present an experimental evaluation of the Vertica relational database system on a variety of graph analytics, including iterative analysis, a combination of graph and relational analyses, and more complex 1-hop neighborhood graph analytics, showing that it is competitive to two popular vertex-centric graph analytics systems, namely Giraph and GraphLab.
On the Reuse of Past Optimal Queries
(1995)

Vijay V. Raghavan Hayri Sever The Center for Advanced Computer Studies The Department of Computer Science University of Southwestern Louisiana University of Southwestern Louisiana Lafayette, LA 70504, USA Lafayette, LA 70504, USA e-mail: raghavan@cacs.usl. edu Information Retrieval (IR) systems exploit user feedback by generating an optimal query with respect to a particular information need. Since obtaining an optimal query is an expensive process, the need for mechanisms to save and reuse past optimal queries for future queries is obvions. In this article, we propose the use of a query base, a set of persistent past optimal queries, and investigate similarity measures between queries. The query base can be used either to answer user queries or to formulate optimal queries. We justify the former case analytically and the latter case by experiment.
Learning to map between ontologies on the semantic web
(2002)

Ontologies play a prominent role on the Semantic Web. They make possible the widespread publication of machine understandable data, opening myriad opportunities for automated information processing. However, because of the Semantic Web's distributed nature, data on it will inevitably come from many different ontologies. Information processing across ontologies is not possible without knowing the semantic mappings between their elements. Manually finding such mappings is tedious, error-prone, and clearly not possible at the Web scale. Hence, the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web.We describe glue, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology glue finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures, and show that glue can work with all of them. This is in contrast to most existing approaches, which deal with a single similarity measure. Another key feature of glue is that it uses multiple learning strategies, each of which exploits a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend glue to incorporate commonsense knowledge and domain constraints into the matching process. For this purpose, we show that relaxation labeling, a well-known constraint optimization technique used in computer vision and other fields, can be adapted to work efficiently in our context. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains, and show that glue proposes highly accurate semantic mappings.
Determining Semantic Similarity among Entity Classes from Different Ontologies
(2003)

Semantic similarity measures play an important role in information retrieval and information integration. Traditional approaches to modeling semantic similarity compute the semantic distance between definitions within a single ontology. This single ontology is either a domain-independent ontology or the result of the integration of existing ontologies. We present an approach to computing semantic similarity that relaxes the requirement of a single ontology and accounts for differences in the levels of explicitness and formalization of the different ontology specifications. A similarity function determines similar entity classes by using a matching process over synonym sets, semantic neighborhoods, and distinguishing features that are classified into parts, functions, and attributes. Experimental results with different ontologies indicate that the model gives good results when ontologies have complete and detailed representations of entity classes. While the combination of word matching and semantic neighborhood matching is adequate for detecting equivalent entity classes, feature matching allows us to discriminate among similar, but not necessarily equivalent, entity classes.
Issues in Stacked Generalization
(1999)

Stacked generalization is a general method of using a high-level model to combine lowerlevel models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a `black art' in classi cation tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. We nd that best results are obtained when the higher-level model combines the con dence (and not just the predictions) of the lower-level ones. We demonstrate the e ectiveness of stacked generalization for combining three di erent types of learning algorithms for classi cation tasks. We also compare the performance of stacked generalization with majority vote and published results of arcing and bagging.
You are what you say: privacy risks of public mentions
(2006)

In today's data-rich networked world, people express many aspects of their lives online. It is common to segregate different aspects in different places: you might write opinionated rants about movies in your blog under a pseudonym while participating in a forum or web site for scholarly discussion of medical ethics under your real name. However, it may be possible to link these separate identities, because the movies, journal articles, or authors you mention are from a sparse relation space whose properties (e.g., many items related to by only a few users) allow re-identification. This re-identification violates people's intentions to separate aspects of their life and can have negative consequences; it also may allow other privacy violations, such as obtaining a stronger identifier like name and address.This paper examines this general problem in a specific setting: re-identification of users from a public web movie forum in a private movie ratings dataset. We present three major results. First, we develop algorithms that can re-identify a large proportion of public users in a sparse relation space. Second, we evaluate whether private dataset owners can protect user privacy by hiding data; we show that this requires extensive and undesirable changes to the dataset, making it impractical. Third, we evaluate two methods for users in a public forum to protect their own privacy, suppression and misdirection. Suppression doesn't work here either. However, we show that a simple misdirection strategy works well: mention a few popular items that you haven't rated.
Development of a deep silicon phase Fresnel lens using Gray-scale lithography and deep reactive ion etching
(2004)

We report the first fabrication and development of a deep phase Fresnel lens (PFL) in silicon through the use of gray-scale lithography and deep-reactive ion etching (DRIE). A Gaussian tail approximation is introduced as a method of predicting the height of photoresist gray levels given the relative amount of transmitted light through a gray-scale optical mask. Device mask design is accomplished through command-line scripting in a CAD tool to precisely define the millions of pixels required to generate the appropriate profile in photoresist. Etch selectivity during DRIE pattern transfer is accurately controlled to produce the desired scaling factor between the photoresist and silicon profiles. As a demonstration of this technology, a 1.6-mm diameter PFL is etched 43 /spl mu/m into silicon with each grating profile designed to focus 8.4 keV photons a distance of 118 m.
A Comparative Analysis of Enterprise Architecture Frameworks Based on EA Quality Attributes
(2009)

Many Enterprise Architecture Frameworks (EAFs) are in use today to guide or serve as a model for the enterprise architecture development. Researchers offer general comparative information about EAF. However, there has been little work on the quality of Enterprise Architecture (EA). This study provides the characteristics of the five EAFs using comparative analysis based on Enterprise Architecture Quality Attributes (EAQAs). The attribute of EA quality was extracted from the EAFs and the definition of EAQA was defined from EA user viewpoint. The criteria for each quality attribute were developed to compare EAFs using four dimensional concepts. This paper compared several frameworks using the criteria to provide guidance in the selection of an EAF that meets the quality requirement of EA
Rational Choice Theory : Toward a Psychological , Social , and Material Contextualization of Human Choice Behavior
(2016)

The main purpose of this paper is to provide a brief overview of the rational choice approach, followed by an identification of several of the major criticisms of RCT and its conceptual and empirical limitations. It goes on to present a few key initiatives to develop alternative, more realistic approaches which transcend some of the limitations of Rational Choice Theory (RCT). Finally, the article presents a few concluding reflections and a table comparing similarities and differences between the mainstream RCT and some of the initial components of an emerging choice theory. Our method has been to conduct a brief selective review of rational choice theoretical formulations and applications as well as a review of diverse critical literature in the social sciences where rational choice has been systematically criticized. We have focused on a number of leading contributors (among others, several Nobel Prize Recipients in economics, who have addressed rational choice issues). So this article makes no claim for completeness. The review maps a few key concepts and assumptions underpinning the conceptual model and empirical applications of RCT. It reviews also a range of critical arguments and evidence of limitations. It identifies selected emerging concepts and theoretical revisions and adaptations to choice theory and what they entail. The results obtained, based on our literature reviews and analyses, are the identification of several major limitations of RCT as well as selected modifications and adaptations of choice theory which overcome or promise to overcome some of the RCT limitations. Thus, the article with Table 1 in hand provides a point of departure for follow-up systematic reviews and more precise questions for future theory development. The criticisms and adaptations of RCT have contributed to greater realism, empirical relevance, and increased moral considerations. The developments entail, among other things: the now well-known cognitive limitations (“bounded rationality”) and, for instance, the role of satisficing rather than maximizing in decision-making to deal with cognitive T. Burns, E. Roszkowska 196 complexity and the uncertainties of multiple values; choice situations are re-contextualized with psychology, sociology, economic, and material conditions and factors which are taken into account explicitly and insightfully in empirical and theoretical work. Part of the contextualization concerns the place of multiple values, role and norm contradictions, and moral dilemmas in much choice behavior. In concluding, the article suggests that the adaptations and modifications made in choice theory have led to substantial fragmentation of choice theory and as of yet no integrated approach has appeared to simply displace RCT.
SASI: A New Ultralightweight RFID Authentication Protocol Providing Strong Authentication and Strong Integrity
(2007)

As low-cost RFIDs become more and more popular, it is imperative to design ultralightweight RFID authentication protocols to resist all possible attacks and threats. However, all of the previous ultralightweight authentication schemes are vulnerable to various attacks. In this paper, we propose a new ultralightweight RFID authentication protocol that provides strong authentication and strong integrity protection of its transmission and of updated data. The protocol requires only simple bit-wise operations on the tag and can resist all the possible attacks. These features make it very attractive to low-cost RFIDs and very low-cost RFIDs.
Self-Supervised Generative Adversarial Networks
(2018)

Conditional GANs are at the forefront of natural image synthesis. The main drawback of such models is the necessity for labelled data. In this work we exploit two popular unsupervised learning techniques, adversarial training and self-supervision, to close the gap between conditional and unconditional GANs. In particular, we allow the networks to collaborate on the task of representation learning, while being adversarial with respect to the classic GAN game. The role of self-supervision is to encourage the discriminator to learn meaningful feature representations which are not forgotten during training. We test empirically both the quality of the learned image representations, and the quality of the synthesized images. Under the same conditions, the self-supervised GAN attains a similar performance to stateof-the-art conditional counterparts. Finally, we show that this approach to fully unsupervised learning can be scaled to attain an FID of 33 on unconditional IMAGENET generation.
Digital Transformation in the Automotive Industry: towards a Generic Value Network
(2017)

The emergence of digital innovations is accelerating and intervening existing business models by delivering opportunities for new services. Drawing on the automotive industry, leading trends like self-driving cars, connectivity and car sharing are creating new business models. These are simultaneously giving rise for innovative market entrants, which begin to transform the automotive industry. However, literature does not provide a generic value network of the automotive industry, including new market players. The paper aims to visualize the current automotive ecosystem, by evolving a generic value network using the e3-value method. We define different roles, which are operating in the automotive industry by analyzing 650 companies reported in the Crunchbase database and present the value streams within the ecosystem. To validate the proposed generic value network we conducted five preliminary interviews with experts from the automotive industry. Our results show the central role of mobility service platforms, emerging disruptive technology providers and the dissemination of industries, e.g., as OEMs collaborate with mobile payment providers. Scholars in this field can apply the developed generic value network for further research, while car manufacturers may apply the model to position themselves in their market and to identify possible disruptive actors or potential business opportunities.
Design of RYSEN: An Intrinsically Safe and Low-Power Three-Dimensional Overground Body Weight Support
(2018)

Body weight support (BWS) systems are widely used in gait research and rehabilitation. This letter introduces a new three-dimensional overground BWS system, called the RYSEN. The RYSEN is designed to be intrinsically safe and low power consuming, while still performing at least as well as existing BWS systems regarding human–robot interaction. These features are mainly achieved by decoupling degrees of freedom between motors: slow/high-torque motors for vertical motion and fast/low-torque motors for horizontal motion. This letter explains the design and evaluates its performance on power consumption and safety. Power consumption is expressed in terms of the sum of the positive mechanical output power of all motor axes. Safety is defined as the difference between the mechanical power available for horizontal and vertical movements and the mechanical power that is needed to perform its task. The results of the RYSEN are compared to the performance of three similar systems: a gantry, the FLOAT, and a classic cable robot. The results show that the RYSEN and a gantry consume approximately the same amount of power. The amount is approximately half the power consumed by the next-best system. For the safety, the gantry is taken as the benchmark, because of its perfect decoupling of directions. The RYSEN has a surplus of 268 W and 126 W for horizontal and vertical movements, respectively. This is significantly lower than the next-best system, which has a surplus of 1088 W and 1967 W, respectively.
Interpretable and effective opinion spam detection via temporal patterns mining across websites
(2016)

Millions of ratings and reviews on online review websites are influential over business revenues and customer experiences. However, spammers are posting fake reviews in order to gain financial benefits, at the cost of harming honest businesses and customers. Such fake reviews can be illegal and it is important to detect spamming attacks to eliminate unjust ratings and reviews. However, most of the current approaches can be incompetent as they can only utilize data from individual websites independently, or fail to detect more subtle attacks even they can fuse data from multiple sources. Further, the revealed evidence fails to explain the more complicated real world spamming attacks, hindering the detection processes that usually have human experts in the loop. We close this gap by introducing a novel framework that can jointly detect and explain the potential attacks. The framework mines both macroscopic level temporal sentimental patterns and microscopic level features from multiple review websites. We construct multiple sentimental time series to detect atomic dynamics, based on which we mine various cross-site sentimental temporal patterns that can explain various attacking scenarios. To further identify individual spams within the attacks with more evidence, we study and identify effective microscopic textual and behavioral features that are indicative of spams. We demonstrate via human annotations, that the simple and effective framework can spot a sizable collection of spams that have bypassed one of the current commercial anti-spam systems.